Method, apparatus, and artificial intelligence editor for implementing artificial intelligence behavior

ABSTRACT

Methods, apparatus and artificial intelligence (AI) editors for implementing an AI behavior are provided herein. In an exemplary method, an AI behavior configuration file can be obtained. The AI behavior configuration file can be configured using at least one preset component, and the AI behavior configuration file matches logic of a preset AI behavior. It can be tested whether a result of running the AI behavior configuration file reaches a preset effect. When the result of running the AI behavior configuration file reaches the preset effect, the preset AI behavior can be implemented according to the AI behavior configuration file.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation application of PCT Patent ApplicationNo. PCT/CN2014/080399, filed on Jun. 20, 2014, which claims priority toChinese Patent Application No. 201310632412.2, filed on Nov. 29, 2013,the entire contents of which are incorporated herein by reference.

FIELD OF THE DISCLOSURE

The present disclosure generally relates to the field of artificialintelligence and, more particularly, relates to methods, apparatus, andartificial intelligence editors for implementing an artificialintelligence behavior.

BACKGROUND

Artificial intelligence (AI) refers to technologies that simulate humanthinking and action by using modern tools such as computers. Withcontinuous advancement of AI technologies, AI technologies have beenapplied to various aspects of industrial manufacturing and human life.

For example, when the AI technologies are applied to a game applicationprogram, an entity having human-like behaviors is generated. The entityis an AI agent. Because the AI agent can exhibit intelligent behaviorsand activities similar to intelligent behaviors and activities of human,or characteristics consistent with a player's thinking and perception,the AI agent can improve playability of the game application program.

During the design of an application program related to AI, the focus ofthe design of the application program related to AI is how to make an AIagent implement a certain AI behavior. When a simple method forimplementing an AI behavior is used, efficiency of designing theapplication pro gram related to AI can be improved.

Conventionally, during the design of an application program related toAI, a method typically used for implementing an AI behavior includes thefollowing steps. First, an AI planner designs logic of an AI behavior tobe implemented by an AI agent. Next, a program developer compiles thelogic of the AI behavior to be implemented by the AI agent intocorresponding code. Then, the planner runs the compiled code and detectswhether the compiled code can reach or generate preset effects of the AIbehavior. When the AI agent generates the preset effects of the AIbehavior, the AI behavior of the AI agent is thus implemented.Otherwise, when the AI agent does not generate the preset effects of theAI behavior, the code needs to be compiled and debugged until the AIagent generates the preset effects of the AI behavior.

However, existing technologies have various problems. For example, whenexisting technology is used for implementing an AI behavior, code needsto be compiled and constantly adjusted. Therefore, a development cycleneeded for implementing an AI behavior is often long, and efficiency ofimplementing an AI behavior is low. In addition, when a certain AIbehavior needs to be added to or deleted from a certain applicationprogram, code needs to be modified in order to implement the adding ordeleting. Further, after the adding or deleting of the AI behavior, thecode needs to be compiled and debugged again. Modifying the code isoften cumbersome. The invested time and labor cost is thus significant.

BRIEF SUMMARY OF THE DISCLOSURE

One aspect of the present disclosure includes methods for implementingan artificial intelligence (AI) behavior. In an exemplary method, an AIbehavior configuration file can be obtained. The AI behaviorconfiguration file can be configured using at least one presetcomponent, and the AI behavior configuration file matches logic of apreset AI behavior. It can be tested whether a result of running the AIbehavior configuration file reaches a preset effect. When the result ofrunning the AI behavior configuration file reaches the preset effect,the preset AI behavior can be implemented according to the AI behaviorconfiguration file.

Another aspect of the present disclosure includes apparatus forimplementing an AI behavior. An exemplary apparatus can include anobtaining module, a testing module, and an implementing module. Theobtaining module is configured to obtain an AI behavior configurationfile. The AI behavior configuration file can be configured using atleast one preset component and the AI behavior configuration filematches logic of a preset AI behavior. The testing module is configuredto test whether a result of running the AI behavior configuration filereaches a preset effect. The implementing module is configured to, whenthe result of running the AI behavior configuration file reaches thepreset effect, implement the preset AI behavior according to the AIbehavior configuration file.

Another aspect of the present disclosure includes a non-transitorycomputer-readable medium having computer program. When being executed bya processor, the computer program performs a method for a method forimplementing an artificial intelligence (AI) behavior. The methodincludes obtaining an AI behavior configuration file, wherein the AIbehavior configuration file is configured using at least one presetcomponent and the AI behavior configuration file matches logic of apreset AI behavior; testing whether a result of running the AI behaviorconfiguration file reaches a preset effect; and implementing the presetAI behavior according to the AI behavior configuration file when theresult of running the AI behavior configuration file reaches the preseteffect.

Other aspects of the present disclosure can be understood by thoseskilled in the art in light of the description, the claims, and thedrawings of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings are merely examples for illustrative purposesaccording to various disclosed embodiments and are not intended to limitthe scope of the disclosure.

FIG. 1 depicts an exemplary environment for implementing methods forimplementing an AI behavior in accordance with various disclosedembodiments;

FIG. 2 depicts a flow diagram of an exemplary method for implementing anAI behavior in accordance with various disclosed embodiments;

FIG. 3 depicts a flow diagram of another exemplary method forimplementing an AI behavior in accordance with various disclosedembodiments;

FIG. 4 depicts a structure diagram of an exemplary apparatus forimplementing an AI behavior in accordance with various disclosedembodiments;

FIG. 5 depicts a structure diagram of another exemplary apparatus forimplementing an AI behavior in accordance with various disclosedembodiments;

FIG. 6 depicts a structure diagram of an exemplary testing module inaccordance with various disclosed embodiments;

FIG. 7 depicts a structure diagram of another exemplary apparatus forimplementing an AI behavior in accordance with various disclosedembodiments;

FIG. 8 depicts an exemplary computer system consistent with thedisclosed embodiments;

FIG. 9 depicts another exemplary environment for implementing methodsfor implementing an AI behavior in accordance with various disclosedembodiments;

FIG. 10 depicts another exemplary environment for implementing methodsfor implementing an AI behavior in accordance with various disclosedembodiments; and

FIG. 11 depicts an exemplary interactive process between an AI editorand an AI system in accordance with various disclosed embodiments.

DETAILED DESCRIPTION

Reference will now be made in detail to exemplary embodiments of thedisclosure, which are illustrated in the accompanying drawings.

Various embodiments provide methods, apparatus, and artificialintelligence (AI) editors for implementing an AI behavior. The methodsdisclosed herein can involve an AI editor, one or more AI servers, an AIbehavior testing apparatus, and/or node configuration. In someembodiments, each of the AI editor, the AI server, the AI behaviortesting apparatus, and the node configuration can be implemented on aseparate computer system. In other embodiments, some or all of the AIeditor, the AI server, the AI behavior testing apparatus, and the nodeconfiguration can be implemented on one computer system.

When any of the AI editor, the AI server and the node configuration areimplemented by separate computer systems, the separate computer systemscan be coupled via a communication network for information exchange,e.g., sending/receiving result of running an AI behavior configurationfile, sending/receiving application program environment data,sending/receiving an AI behavior configuration file, callingencapsulated components, etc.

The communication network may include any appropriate type ofcommunication network for providing network connections to the computersystems that implement the AI editor, the AI server, the AI behaviortesting apparatus, and/or the node configuration. For example, thecommunication network may include the Internet or other types ofcomputer networks or telecommunication networks, either wired orwireless.

An AI server, as used herein, may refer to one or more server computersconfigured to control an AI agent to perform an AI behavior matching acurrent environment in an application program. The AI server can performsuch a function for one of one or more application programs. On one AIserver, AI system of at least one application pro gram can be mounted.For example, one application program can have one corresponding AIsystem.

FIG. 8 shows a block diagram of an exemplary computing system 800capable of implementing the AI editor, the AI server, the AI behaviortesting apparatus, and/or the node configuration. As shown in FIG. 8,the exemplary computer system 800 may include a processor 802, a storagemedium 804, a monitor 806, a communication module 808, a database 810,peripherals 812, and one or more bus 814 to couple the devices together.Certain devices may be omitted and other devices may be included. Invarious embodiments, the computer system 800 may include one or moreprocessors to execute computer programs in parallel.

The processor 802 can include any appropriate processor or processors.Further, the processor 802 can include multiple cores for multi-threador parallel processing. The storage medium 804 may include memorymodules, e.g., Read-Only Memory (ROM), Random Access Memory (RAM), andflash memory modules, and mass storages, e.g., CD-ROM, U-disk, removablehard disk, etc. The storage medium 804 may store computer programs forimplementing various processes (e.g., encapsulating code intocomponents, configuring an AI behavior configuration file, testing an AIbehavior configuration file, implementing an AI behavior, etc.), whenexecuted by the processor 802.

The monitor 806 may include display devices for displaying contents inthe computing system 800, e.g., providing a preview of effect of runningthe AI behavior, or displaying a game interface. The peripherals 812 mayinclude I/O devices such as keyboard and mouse.

Further, the communication module 808 may include network devices forestablishing connections through the communication network. The database810 may include one or more databases for storing certain data and forperforming certain operations on the stored data, e.g., storing AIbehavior configuration files, storing encapsulated components, managingAI rule library, etc.

With rapid development of computer technology, more and more applicationprograms are applied to human life and bringing great convenience andfun into human life. With rapid development of AI technologies, more andmore AI technologies are applied to application programs on modern toolssuch as computers, thus enabling the modern tools such as computers tosimulate and perform behaviors similar to human thinking and humanbehaviors. The application programs can thus become increasinglyintelligent and entertaining.

As used herein, wherever applicable, ‘application pro gram can be usedinterchangeably with ‘application’. An application or application program can refer to any appropriate software program that accomplishes acertain specific purpose.

In an application related to AI technologies, human thinking and actioncan usually be given to one or more entities. Such an entity is an AIagent. An AI agent generally refers to an entity that has goal(s),behavior(s) and knowledge, and operates independently in a certainenvironment. A typical example of an AI agent is a monster or non-playercharacter (NPC) in an application. In addition, during operation of anapplication program involving interaction, e.g., battle, between humanand computer, an AI agent can be a role played by the computer. Abehavior performed by an AI agent that is similar to human thinking andhuman behavior is referred to as an AI behavior.

During the planning of an application related to AI technologies, thekey to designing the application related to AI is how to make an AIagent exhibit a certain AI behavior. For example, in a type ofapplication programs including chess or playing cards between a humanand a computer, major consideration for designing such a type ofapplication programs includes how to make an AI agent played by thecomputer implement an AI behavior of playing a card. In addition, inapplication programs involving a monster, major consideration fordesigning such a type of application programs includes how to make themonster played by the computer exhibit AI behaviors including, e.g.,attacking, chasing, and any other appropriate behaviors. In order tomake an AI agent exhibit a certain AI behavior, the AI behavior needs tobe designed according to the application program, and it needs to beensured that the designed AI behavior can be implemented.

For ease of description, in various embodiments where an applicationprogram of playing cards is described, a playing card can be referred toas a card. ‘A playing card having a symbol 10-of-Hearts’ can also bereferred to as ‘a 10-of-Hearts card’. Playing cards having other symbolscan be referred to in a similar manner. ‘Playing a playing card’ can bereferred to as ‘playing a card’.

To implement an AI behavior, logic relationship(s) related to AIbehaviors of an AI agent needs to be designed for various AI agents.Next, a program compiler can compile the logic relationship intocorresponding code. The code can be run to test whether the AI behaviorcan be implemented. Thus, a process for implementing the AI behavior mayneed the planner and the program compiler to participate simultaneously.A development cycle can thus be long.

In view of the problems set forth above and other problems, variousembodiments provide a method for implementing an AI behavior. FIG. 1depicts an exemplary environment for implementing methods forimplementing an AI behavior in accordance with various disclosedembodiments. As shown in FIG. 1, various components can be stored innode configuration. The components have been encapsulated beforehand.During the storing of the components, contents including, e.g., type,function, detailed information, initialization parameter(s), andmounting topology of each component can be stored in the nodeconfiguration at the same time. During the design of an AI behavior,preset components can be called from the node configuration by an AIeditor, to configure an AI behavior configuration file for implementingthe AI behavior. After the AI behavior configuration file forimplementing the AI behavior is configured, the configured AI behaviorconfiguration file can be run via an AI behavior testing apparatus. Aresult of running the AI behavior configuration file can be previewed.It can thus be determined whether the result of running the AI behaviorconfiguration file is consistent with a preset result of running the AIbehavior.

The disclosed methods for implementing an AI behavior can be detailed inthe following examples. FIG. 2 depicts a flow diagram of an exemplarymethod for implementing an AI behavior in accordance with variousdisclosed embodiments.

Referring to FIG. 2, in Step 101, an AI behavior configuration file isobtained. The AI behavior configuration file can be configured andgenerated using at least one preset component. The AI behaviorconfiguration file can match logic of a preset AI behavior.

The preset AI behavior can refer to an AI behavior that a plannerdesires to implement, i.e., to have an AI agent to perform or implementunder a certain condition according to the needs of designing anapplication program. The logic of the preset AI behavior can refer toany appropriate relation between the preset AI behavior and condition ordata of the application program. The logic can be pre-designed with thepreset AI behavior.

In Step 102, it is tested whether a result of running the AI behaviorconfiguration file reaches a preset effect. In Step 103, when the resultof running the AI behavior configuration file reaches the preset effect,the preset AI behavior is implemented based on the AI behaviorconfiguration file. In various embodiments, implementing the AI behaviorcan include completing the configuring of the AI behavior configurationfile and optionally storing it in an appropriate rule base on anappropriate server.

As used herein, wherever applicable, ‘reaching a preset effect’ refersto generating an effect that is similar to, same as, and/or consistentwith the preset effect. The preset effect can be pre-determined duringthe design of the preset AI behavior.

Optionally, before the AI behavior configuration file is obtained, themethod can further include the following steps. Code for implementingspecific functions is encapsulated into components for implementingspecific functions. The components, and type(s), function(s), detailedinformation, initialization parameter(s), and mounting topology of thecomponents are stored in the node configuration. The obtaining of the AIbehavior configuration file can include calling at least one presetcomponent from the node configuration to configure the AI behaviorconfiguration file.

Optionally, the obtaining of the AI behavior configuration file caninclude the following steps. At least one preset component can be calledfrom the node configuration to configure a first AI behaviorconfiguration file. A second AI behavior configuration file can beobtained from an AI rule library. The AI rule library can have at leastone AI behavior configuration file pre-stored, i.e., stored beforehand.The first AI behavior configuration file and the second AI behaviorconfiguration file can then be combined to obtain the AI behaviorconfiguration file.

Optionally, the obtaining of the AI behavior configuration file caninclude obtaining the AI behavior configuration file from the AI rulelibrary. The AI rule library can have at least one AI behaviorconfiguration file pre-stored.

Optionally, after calling at least one preset component from the nodeconfiguration to configure the AI behavior configuration file, theconfigured AI behavior configuration file can be stored in the AI rulelibrary.

Optionally, the testing of whether a result of running the AI behaviorconfiguration file reaches a preset effect can include the followingsteps. Application program environment data related to the preset effectcan be loaded. The AI behavior configuration file can be run byintegrating the application program environment data. It can bepreviewed whether the result of running the AI behavior configurationfile is consistent with the preset effect. When the result of runningthe AI behavior configuration file is consistent with the preset effect,the result of running the AI behavior configuration file reaches thepreset effect.

In various embodiments, when the AI behavior configuration file is runby integrating the application program environment data, the applicationprogram environment data can be used as parameters (e.g., environmentdata of a game application program) needed for running the AI behaviorconfiguration file within a game environment during the testing.

Optionally, after the preset AI behavior is implemented based on the AIbehavior configuration file, the method can further include thefollowing steps. The AI behavior configuration file can be sent to an AIserver. Thus, the AI server can update the AI rule library that storesthe AI behavior configuration file. The AI server can have AI system(s)of at least one application. For example, the at least one applicationcan each have one AI system. The AI system can include an AI rulelibrary. The AI rule library can store at least one AI behaviorconfiguration file.

Using the methods in accordance with various embodiments, an AI behaviorconfiguration file can be obtained. The AI behavior configuration filecan be configured and generated using at least one preset component. Itcan be tested whether a result of running the AI behavior configurationfile reaches a preset effect. Thus, an AI behavior does not have to beimplemented by compiling code. Operation of implementing the AI behaviorcan be simple. The development cycle for implementing the AI behaviorcan be shortened. The efficiency of implementing the AI behavior can beimproved. In addition, when a certain AI behavior needs to be added toor deleted from a certain application, such adding or deleting can beaccomplished simply by modifying a component that is called. The code nolonger needs to be modified. Thus, time and labor cost to be investedcan be reduced.

FIG. 3 depicts a flow diagram of another exemplary method forimplementing an AI behavior in accordance with various disclosedembodiments. For illustrative purposes, in certain examples, the methodfor implementing an AI behavior can be executed by an AI editor. Themethod can include the following exemplary steps.

In Step 201, code for implementing specific functions is encapsulatedinto components for implementing specific functions. The components, andtype(s), function(s), detailed information, initialization parameter(s),and mounting topology of the components are stored in nodeconfiguration.

In order to implement an AI behavior, corresponding code needs to becompiled according to logic of the AI behavior to be implemented.Further, the code needs to be run to test and determine whether the AIbehavior can be implemented. Thus, a process for implementing the AIbehavior is often complicated. A development cycle can thus be long.

However, by using the methods disclosed herein, before implementing anAI behavior, via certain encapsulating tools or means, code forimplementing specific functions can be encapsulated into component(s)for implementing the specific functions. An encapsulated component canhave a function independent of other components. Interface betweencomponents can be specified by a contract. A component can have a cleardependence on context, can be deployed independently, and can beassembled.

Methods for encapsulating the code for implementing specific functionsinto the component(s) for implementing the specific functions can be anyappropriate methods, and are not limited in the present disclosure. Thetools or means used for the encapsulating can be any appropriate toolsor means, and are not limited in the present disclosure. For example,the AI editor may encapsulate the code for implementing specificfunctions into the component(s) for implementing the specific functions.

Further, in various embodiments, in order to quickly determine whichcomponents to call during the subsequent implementing of an AI behavior,the encapsulated components for implementing the specific functions, andtype(s), function(s), detailed information, initialization parameter(s),and mounting topology of the components can be stored in nodeconfiguration at the same time. Thus, when the components aresubsequently being used, the components that are needed can be directlycalled from the node configuration. The components stored in the nodeconfiguration can be called by various different AI agents in the sameapplication, and/or be called by AI agents in different applicationprograms. Thus, the components not only are versatile, but also canimprove efficiency of designing an application program during the designof the application pro gram.

For example, in order to distinguish between different functionsimplemented by different components, during the storing of components,various components can be classified. The functions implemented by thecomponents can be divided into various types. Accordingly, thecomponents can be classified into various types. For example, thecomponents can be classified into classifying component, selectingcomponent, condition component, and/or behavior component.

A condition component can refer to a component that executes a functionof judging condition(s) and returning result of the judging, e.g., acomponent for judging whether a player currently has a K-of-Heartsplaying card. A behavior component can refer to a component thatexecutes a specified function (i.e., a function that is set), e.g., acomponent for playing a K-of-Hearts card. A selecting component canrefer to a component that can select a component for executing based oncertain screening rule(s). The screening rules can include, but are notlimited to, a maximum-weight-selection mode, a random-selection mode, asequential-selection mode, or any other appropriate modes.

In addition, in the methods according to various disclosed embodiments,a planner can directly call one or more components from the nodeconfiguration via the AI editor, or can directly call one or more AIbehavior configuration files pre-stored in an AI rule library, in orderto implement an AI behavior. Therefore, during the storing of thecomponents, detailed information of the components can be stored in thenode configuration. The detailed information of the components caninclude, e.g., description of specific usage of the components. Inaddition, initialization parameters and mounting topology of thecomponents can be stored in the node configuration at the same time. Themounting topology of the components can be designed according to an AIbehavior to be implemented by an AI agent in a specific applicationprogram. For example, a selecting component can be mounted with at leastone behavior component. A behavior component can be mounted at least onecondition component. As used herein, when a first component is mountedwith a second component, the first component has the second componentmounted on the first component.

In Step 202, the AI editor obtains an AI behavior configuration filethat is configured using at least one preset component. The AI behaviorconfiguration file can match logic of a preset AI behavior. In variousembodiments, the AI editor can configure the AI behavior configurationfile by using at least one preset component.

To implement the AI behavior via the AI editor, the planner of the AIbehavior of the application can pre-design or preset the logic of the AIbehavior. Next, via the AI editor, the planner can obtain an AI behaviorconfiguration file that is configured using at least one presetcomponent. The configured AI behavior configuration file needs to matchlogic of the preset AI behavior.

The AI editor can obtain the AI behavior configuration file configuredusing at least one preset component using various methods. During actualimplementations, the obtaining of the AI behavior configuration file caninclude, but are not limited to, the methods described in the followingexamples.

In the first example for obtaining the AI behavior configuration file,the AI editor can call at least one preset component from the nodeconfiguration to configure the AI behavior configuration file. Invarious embodiments, the encapsulated components, and type(s),function(s), detailed information, initialization parameter(s), andmounting topology of the components are stored in the nodeconfiguration. Therefore, during the implementing of the AI behavior,based on the logic of the AI behavior, the AI editor can obtain multiplecomponents matching the logic of the AI behavior. The logic of the AIbehavior can be designed beforehand, i.e., pre-designed. The multiplecomponents can be combined using certain methods that match the logic ofthe AI behavior, to configure and thus generate or obtain the AIbehavior configuration file.

In the second example for obtaining the AI behavior configuration file,at least one preset component can be called from the node configurationto configure a first AI behavior configuration file. A second AIbehavior configuration file can be obtained from an AI rule library. TheAI rule library can have at least one AI behavior configuration filepre-stored, i.e., stored beforehand. The first AI behavior configurationfile and the second AI behavior configuration file can then be combinedto obtain the AI behavior configuration file.

After each time of obtaining an AI behavior configuration file bycombining various components, the AI behavior configuration file thathas already been designed can be stored. Thus, it can be ensured thatthe stored AI behavior configuration file can be directly called toconfigure other AI behavior configuration file(s) that have functionssimilar to the function of the stored AI behavior configuration file, orthat the stored AI behavior configuration file can be combined with oneor more other files to implement other AI behavior(s).

In various embodiments, assuming AI behavior configuration file(s) forimplementing certain functions has already been designed and stored inthe AI rule library. Thus, to obtain the AI behavior configuration file,the AI editor can call one or more AI behavior configuration files thathave been stored in the AI rule library, and call at least one presetcomponent from the node configuration. In this case, the at least onepreset component called from the node configuration and the one or moreAI behavior configuration files called from the AI rule library can bemerged and combined to obtain an AI behavior configuration file forimplementing a certain AI behavior.

In the third example for obtaining the AI behavior configuration file,the AI behavior configuration file can be obtained from the AI rulelibrary. The AI rule library can have at least one AI behaviorconfiguration file pre-stored.

In this case, for a certain AI behavior, when one or more AI behaviorconfiguration files that have been configured and stored in the AI rulelibrary can be directly called to be combined to obtain the AI behaviorconfiguration file for implementing the certain AI behavior, the AIeditor can directly call the one or more AI behavior configuration filesfrom the AI rule library, to configure the AI behavior configurationfile for implementing the certain AI behavior. In addition, the AIeditor can further run the one or more AI behavior configuration filesthat have been configured and stored in the AI rule library, to test oroptimize the one or more AI behavior configuration files or the AIbehaviors corresponding to the one or more AI behavior configurationfiles.

Optionally, when the AI behavior configuration file is configured by theAI editor by calling at least one preset component from the nodeconfiguration (e.g., as shown in the first example for obtaining the AIbehavior configuration), after the AI behavior configuration file isconfigured, the configured AI behavior configuration file can be storedin the AI rule library. Thus, during the subsequent design of AIbehavior configuration files for implementing other AI behaviors, the AIbehavior configuration files for implementing other AI behaviors can beconfigured by integrating the exemplary methods as described above inthe second or the third example for obtaining the AI behaviorconfiguration file.

In Step 203, it is tested whether a result of running the AI behaviorconfiguration file reaches a preset effect. In order to verify whetherthe designed AI behavior configuration file can implement the preset AIbehavior, it is tested whether a result of running the AI behaviorconfiguration file reaches a preset effect. The testing of whether theresult of running the AI behavior configuration file reaches a preseteffect can be accomplished via an AI behavior testing apparatus.

In one embodiment, the AI behavior testing apparatus can be anindependent apparatus that does not depend on the AI editor. In thiscase, after the AI behavior testing apparatus loads the configured AIbehavior configuration file via the AI editor, the AI behavior testingapparatus can run the AI behavior configuration file and test whetherthe result of running the AI behavior configuration file reaches thepreset effect.

In another embodiment, the AI behavior testing apparatus can be integralwith the AI editor, i.e., can be integrated with the AI editor asstructurally a whole. During the testing of whether the result ofrunning the AI behavior configuration file reaches the preset effect,the AI behavior configuration file can be directly imported into the AIbehavior testing apparatus.

The testing of whether the result of running the AI behaviorconfiguration file reaches the preset effect can be accomplished usingvarious methods that are not limited in the present disclosure. Inpractical implementation, the methods can include, but are not limitedto the following exemplary process.

For example, Application program environment data related to the preseteffect can be loaded. The AI behavior configuration file can be run byintegrating the application program environment data. It can bepreviewed whether the result of running the AI behavior configurationfile is consistent with the preset effect. When the result of runningthe AI behavior configuration file is consistent with the preset effect,the result of running the AI behavior configuration file reaches thepreset effect.

By testing in real time whether the result of running the configured AIbehavior configuration file is consistent with the preset effect, the AIbehavior configuration file that is configured in real time can betested in real time. By testing in real time whether the AI behaviorconfiguration file that is configured by calling preset components canimplement the AI behavior, the AI rule library of an application programcan be adjusted and enriched. The AI behavior can be added to or deletedfrom the application program in real time. Further, the developmentcycle of implementing the AI behavior can be shortened. Time and laborcost that is invested can be saved. In addition, using the method inaccordance with various embodiments, an AI behavior of an AI agent undera certain condition can become editable. Operation of implementing theAI behavior can thus be simplified.

In Step 204, when the result of running the AI behavior configurationfile reaches the preset effect, the preset AI behavior is implementedaccording to the AI behavior configuration file. In various embodiments,after the result of running the AI behavior configuration file istested, when the result of running the AI behavior configuration filereaches the preset effect, it can be determined that the AI behaviorconfiguration file configured by calling the components can implementthe preset AI behavior, according to the result of running the AIbehavior configuration file. Further, it can be determined that themethod or process for implement the AI behavior is successful.

On the other hand, after the result of running the AI behaviorconfiguration file is tested, it is found that the result of running theAI behavior configuration file does not reach the preset effect, it isthus proven that the AI behavior configuration file that has beenconfigured cannot implement the preset AI behavior. The AI behaviorconfiguration file thus needs to be configured again, i.e., to bere-configured. In this case, the AI behavior configuration file can bere-configured using the steps or processes as described in variousembodiments, e.g., Steps 202-204, until the result of running theconfigured AI behavior configuration file reaches the preset effect.

For illustrative purposes, the testing of whether a result of running anAI behavior configuration file reaches a preset effect can be explainedin the following example. In the example, the AI behavior can include abehavior of an AI agent playing a 10-of-Hearts playing card.

First, the AI editor can load application program environment datarelated to a preset behavior of playing a 10-of-Hearts card, and load anAI behavior configuration file that is configured by using multiplecomponents. Next, the AI editor can send the AI behavior configurationfile to the AI behavior testing apparatus. The AI behavior testingapparatus can then run the AI behavior configuration file. The AIbehavior testing apparatus can include a display, i.e., a monitor. Themonitor can be configured to provide a preview of effect of the AIbehavior, i.e., a preview of a result of running the AI behaviorconfiguration file. It can be previewed whether the AI agent can executethe AI behavior of playing the 10-of-Hearts card. When the AI agentexecutes the AI behavior of playing the 10-of-Hearts playing card atthis time, it is proven that the AI behavior of playing the 10-of-Heartscard has been successfully operated.

Optionally, because the disclosed methods for implementing an AIbehavior can be used to implement various AI behaviors, after a presetAI behavior is implemented according to an AI behavior configurationfile, the AI behavior configuration file for implementing the preset AIcan be sent to an AI server. The AI server can update the AI rulelibrary that stores the AI behavior configuration file.

An AI server can include a server that controls an AI agent to performan AI behavior matching a current environment in an application. The AIserver can perform such controlling for one of one or more applicationprograms. On one AI server, AI system of at least one applicationprogram can be mounted. For example, one application program can haveone corresponding AI system.

The AI system of each application can include at least one AI rulelibrary that can implement AI behaviors of the each application. The AIrule library can store one or more AI behavior configuration files, andeach AI behavior configuration file can implement at least one functionof an application. Each AI behavior configuration file can be obtainedby calling at least one component in the node configuration.

By using the above-disclosed method or process of updating the AI rulelibrary of the application, during the updating of the AI behavior ofthe application, there is no need to re-release a new version of theapplication program or to patch the application. Thus, the operation ofupdating the application pro gram can be more convenient.

In addition, when different application programs are to implement acertain identical function, identical components can be used toconfigure AI behavior configuration files. Further, an AI system of anapplication can be designed by calling components to configure an AIbehavior configuration file and further by combining AI behaviorconfiguration files. Thus, it is no longer necessary to compile code foreach application program in order to implement the each applicationprogram. Thus, efficiency of designing the AI system of the applicationprogram can be improved.

Using the methods in accordance with various embodiments, an AI behaviorconfiguration file can be obtained. The AI behavior configuration filecan be configured and generated using at least one preset component. Itcan be tested whether a result of running the AI behavior configurationfile reaches a preset effect. Thus, an AI behavior does not have to beimplemented by compiling code. Operation of implementing the AI behaviorcan be simplified. The development cycle for implementing the AIbehavior can be shortened. The efficiency of implementing the AIbehavior can be improved. In addition, when a certain AI behavior needsto be added to or deleted from a certain application, such adding ordeleting can be accomplished simply by modifying a component that iscalled. The code no longer needs to be modified. Thus, investment oftime and labor cost can be reduced.

FIG. 4 depicts a structure diagram of an exemplary apparatus forimplementing an AI behavior in accordance with various disclosedembodiments. As shown in FIG. 4, the apparatus can include an obtainingmodule 401, a testing module 402, and/or an implementing module 403.Certain modules may be omitted and other modules may be included.

The obtaining module 401 is configured to obtain an AI behaviorconfiguration file configured using at least one preset component. TheAI behavior configuration file can match logic of a preset AI behavior.

The testing module 402 is configured to test whether a result of runningthe AI behavior configuration file reaches a preset effect. Theimplementing module 403 is configured to implement the preset AIbehavior according to the AI behavior configuration file when the resultof running the AI behavior configuration file reaches the preset effect.

FIG. 5 depicts a structure diagram of another exemplary apparatus forimplementing an AI behavior in accordance with various disclosedembodiments. As shown in FIG. 5, optionally, the apparatus can furtherinclude an encapsulating module 404, and a storing module 405.

The encapsulating module 404 is configured to encapsulate code forimplementing specific functions into components for implementing thespecific functions. The storing module 405 is configured to store thecomponents, and type(s), function(s), detailed information,initialization parameter(s), and mounting topology of the components innode configuration. The obtaining module 401 is configured to call theat least one preset component from the node configuration to configurethe AI behavior configuration file.

Optionally, the obtaining module 401 is configured to call the at leastone preset component from the node configuration to configure a first AIbehavior configuration file. The obtaining module 401 is furtherconfigured to obtain a second AI behavior configuration file from an AIrule library. The AI rule library can have at least one AI behaviorconfiguration file pre-stored, i.e., stored beforehand. The obtainingmodule 401 is further configured to combine the first AI behaviorconfiguration file and the second AI behavior configuration file toobtain the AI behavior configuration file.

Optionally, the obtaining module 401 is configured to obtain the AIbehavior configuration file from the AI rule library. The AI rulelibrary can have at least one AI behavior configuration file pre-stored.

Optionally, the storing module 405 is configured to store the configuredAI behavior configuration file in the AI rule library.

FIG. 6 depicts a structure diagram of another exemplary testing modulein accordance with various disclosed embodiments. As shown in FIG. 6,the testing module 402 can include a loading unit 4021, a running unit4022, a previewing unit 4023, and/or a determining unit 4024. Certainunits may be omitted and other units may be included.

The loading unit 4021 is configured to load application programenvironment data related to the preset effect. The running unit 4022 isconfigured to run the AI behavior configuration file by integrating theapplication program environment data.

The previewing unit 4023 is configured to preview whether the result ofrunning the AI behavior configuration file is consistent with the preseteffect. The determining unit 4024 is configured to determine that theresult of running the AI behavior configuration file reaches the preseteffect when the result of running the AI behavior configuration file isconsistent with the preset effect.

FIG. 7 depicts a structure diagram of another exemplary apparatus forimplementing an AI behavior in accordance with various disclosedembodiments. Optionally, as shown in FIG. 7, the apparatus can furtherinclude a sending module 406. The sending module 406 is configured tosend the AI behavior configuration file to an AI server, such that theAI server can update the AI rule library that stores the AI behaviorconfiguration file. The AI server can be mounted with AI system(s) of atleast one application. The AI system of each application can include anAI rule library. The AI rule library can store one or more AI behaviorconfiguration files.

Using the apparatus in accordance with various embodiments, an AIbehavior configuration file can be obtained. The AI behaviorconfiguration file can be configured and generated using at least onepreset component. It can be tested whether a result of running the AIbehavior configuration file reaches a preset effect. Thus, an AIbehavior does not have to be implemented by compiling code. Operation ofimplementing the AI behavior can be simplified. The development cyclefor implementing the AI behavior can be shortened. The efficiency ofimplementing the AI behavior can be improved. In addition, when acertain AI behavior needs to be added to or deleted from a certainapplication, such adding or deleting can be accomplished simply bymodifying a component that is called. The code no longer needs to bemodified. Thus, investment of time and labor cost can be reduced.

Various embodiments also provide an AI editor for implementing an AIbehavior. The AI editor can include the apparatus for implementing an AIbehavior as described in various disclosed embodiments, e.g., as shownin FIGS. 4-7.

Using the AI editor in accordance with various embodiments, an AIbehavior configuration file can be obtained. The AI behaviorconfiguration file can be configured and generated using at least onepreset component. It can be tested whether a result of running the AIbehavior configuration file reaches a preset effect. Thus, an AIbehavior does not have to be implemented by compiling code. Operation ofimplementing the AI behavior can be simplified. The development cyclefor implementing the AI behavior can be shortened. The efficiency ofimplementing the AI behavior can be improved. In addition, when acertain AI behavior needs to be added to or deleted from a certainapplication, such adding or deleting can be accomplished simply bymodifying a component that is called. The code no longer needs to bemodified. Thus, investment of time and labor cost can be reduced.

When the disclosed apparatus for implementing an AI behavior implementsan AI behavior, the above functional modules are divided merely forillustrative purposes. In practical applications, according to actualneeds, the above-described functions can be allocated to differentfunctional modules to be accomplished. That is, the internal structureof the apparatus can be divided into different functional modules toaccomplished part or all of the above-described functions. Furtherdetails of the apparatus for implementing AI behavior are described inthe methods for implementing an AI behavior according variousembodiments, e.g., as shown in FIGS. 2-3.

In certain embodiments, optionally, the disclosed methods can involve AIrule(s). The AI rules can determine behavior of an AI agent under acertain condition. For example, in a game called ‘Legend of Heroes’,when an AI agent does not have ‘full blood’ (i.e., a certain level ofpoints or energy in the ‘Legend of Heroes’ game), the AI agent plays amedicine card. The medicine card is a certain playing card used in the‘Legend of Heroes’ game.

Optionally, the disclosed methods can involve an AI rule library. The AIrule library can include a configured collection of AI rules that can befor general usage. The AI rules can include, e.g., AI behaviorconfiguration files that match logic of corresponding AI behaviors,respectively. For example, in the ‘Legend of Heroes’ game, the AI rulelibrary can include configured AI trusteeship.

FIG. 9 depicts another exemplary environment for implementing methodsfor implementing an AI behavior in accordance with various disclosedembodiments. As shown in FIG. 9, source code of extension components canbe imported via an AI editing tool into node configuration to be usedfor subsequent loading. The extension components can refer to componentsneeded for extending a game application program, e.g., adding an AIbehavior to the game. The extension components can include, e.g.,selecting component(s), condition component(s), and behaviorcomponent(s). A planner can edit and store AI rules. In addition, theplanner can view editing effect of an AI rule in real time, via an AIsystem unit testing module.

Referring to FIG. 9, an AI editing tool can be referred to as an AIeditor. The AI editor can be configured to edit an AI behavior, andstore related AI logic relationship into AI rule configuration. Invarious embodiments, ‘editing an AI behavior’ can include configuring anAI behavior configuration file for implementing a preset AI behavior.

The ‘AI system unit testing’ module can be referred to as an AI behaviortesting apparatus. The AI behavior testing apparatus can be configuredto load various game environment data, load protocol data related torequesting an AI behavior, search AI rules, test AI behaviorconfiguration file, and verify whether an AI behavior is consistent withexpectation (e.g., whether result of running the AI behaviorconfiguration file reaches a preset effect).

Node configuration can be configured to import standardized componentsvia the AI editing tool. The node configuration can contain type of eachcomponent, usage instructions of the components, type and description ofinitialization parameter of the components, and mounting topology of thecomponents.

FIG. 10 depicts another exemplary environment for implementing methodsfor implementing an AI behavior in accordance with various disclosedembodiments. As shown in FIG. 10, each AI system can include a dynamiclink library that is loaded onto and processed by an AI server. One AIserver can have AI systems of various different games loaded. When AIrules of a certain game are changed (e.g., adding or deleting AIbehavior or AI behavior configuration file), the changing can beaccomplished simply by calling reload configuration (e.g., ReloadC fg)interface on the AI server. Thus, there is no need to stop the game. Thegame logic server does not need to generate an updated version.

The game logic server can be configured to provide service of specificgame-play logic for multiple clients, synchronize game environment datato a corresponding AI system on the AI server, request a matching AIbehavior (i.e., an AI behavior matching game environment data), andreceive the result of the operation from the AI server. For example, inthe Legend of Heroes game, the game logic server can be MainSvr.

An AI server can be mounted with any AI systems of various games. EachAI system can be loaded with specified AI rule library or AI rulelibraries, and can cache or buffer the matching game environment data.For example, in the Legend of Heroes game, the AI server can be AISvr.

Based on actual applications, AI rule configuration as shown in FIGS.9-10 can either refer to AI rule library having an AI behaviorconfiguration file stored, or refer to an AI behavior configuration filethat is stored in the AI rule library.

FIG. 11 depicts an exemplary interactive process between an AI editorand an AI system in accordance with various disclosed embodiments. Asshown in FIG. 11, the interactive process can include the followingexemplary steps.

In Step 1, an AI editor creates new AI configuration, or loads existingAI configuration, to edit the AI configuration and store the AIconfiguration. In Step 2, the AI system unit testing module debugs theedited AI configuration, runs the edited AI configuration, and analyzesresult of running the edited AI configuration. Steps 1-2 can berepeatedly performed, until the AI behavior implemented by the AIconfiguration is desirable. The AI configuration can refer to an AIbehavior or an AI behavior configuration file corresponding to an AIbehavior.

In Step 3, the AI configuration is sent to the AI server via filetransfer, or via a method for sending configuration downwards. The AIserver can then call the reload configuration (e.g., ReloadCfg)interface of the AI system of the corresponding game, and can thusimplement dynamic updating of the AI rules, i.e., AI rule library. TheAI configuration as shown in FIG. 11 can refer to the AI ruleconfiguration as depicted in FIGS. 9-10.

Part or all of the steps in the methods in accordance with variousembodiments can be accomplished using hardware, or using aprogram/software to instruct related hardware. The program/software canbe stored in a non-transitory computer-readable storage mediumincluding, e.g., ROM/RAM, magnetic disk, optical disk, etc.

The embodiments disclosed herein are exemplary only. Other applications,advantages, alternations, modifications, or equivalents to the disclosedembodiments are obvious to those skilled in the art and are intended tobe encompassed within the scope of the present disclosure.

INDUSTRIAL APPLICABILITY AND ADVANTAGEOUS EFFECTS

Without limiting the scope of any claim and/or the specification,examples of industrial applicability and certain advantageous effects ofthe disclosed embodiments are listed for illustrative purposes. Variousalternations, modifications, or equivalents to the technical solutionsof the disclosed embodiments can be obvious to those skilled in the artand can be included in this disclosure.

The disclosed methods, apparatus and artificial intelligence (AI)editors for implementing an AI behavior can be used in a variety ofcomputer applications. The computer applications can include anyapplication programs that involve AI technology. The applicationprograms can include, but are not limited to, games, online transaction,and computer-aided interactive learning.

Using the methods in accordance with various embodiments, an AI behaviorconfiguration file can be obtained. The AI behavior configuration filecan be configured and generated using at least one preset component. Itcan be tested whether a result of running the AI behavior configurationfile reaches a preset effect. Thus, an AI behavior does not have to beimplemented by compiling code. Operation of implementing the AI behaviorcan be simplified. The development cycle for implementing the AIbehavior can be shortened. The efficiency of implementing the AIbehavior can be improved. In addition, when a certain AI behavior needsto be added to or deleted from a certain application, such adding ordeleting can be accomplished simply by modifying a component that iscalled. The code no longer needs to be modified. Thus, investment oftime and labor cost can be reduced.

By using the methods disclosed herein, before implementing an AIbehavior, via certain encapsulating tools or means, code forimplementing specific functions can be encapsulated into components forimplementing the specific functions. An encapsulated component can havea function independent of other components. Interface between componentscan be specified by a contract. A component can have a clear dependenceon context, can be deployed independently, and can be assembled.

Further, in various embodiments, in order to quickly determine whichcomponents to call during the subsequent implementing of an AI behavior,the encapsulated components for implementing the specific functions, andtypes, functions, detailed information, initialization parameters, andmounting topology of the components can be stored in node configurationat the same time. Thus, when the components are subsequently being used,the components that are needed can be directly called from the nodeconfiguration. The components stored in the node configuration can becalled by various different AI agents in the same application, and/or becalled by AI agents in different application programs. Thus, thecomponents not only are versatile, but also can improve efficiency ofdesigning an application program during the design of the applicationpro gram.

By using the disclosed methods, apparatus, and AI editors forimplementing an AI behavior, during the updating of the AI behavior ofthe application, there is no need to re-release a new version of theapplication program or to patch the application. Thus, the operation ofupdating the application program can be more convenient.

In addition, when different application programs are to implement acertain identical function, identical components can be used toconfigure AI behavior configuration files. Further, an AI system of anapplication can be designed by calling components to configure an AIbehavior configuration file and further by combining AI behaviorconfiguration files. Thus, it is no longer necessary to compile code foreach application program in order to implement the each applicationprogram. Thus, efficiency of designing the AI system of the applicationprogram can be improved.

What is claimed is:
 1. A method for implementing an artificialintelligence (AI) behavior, comprising: obtaining an AI behaviorconfiguration file, wherein the AI behavior configuration file isconfigured using at least one preset component and the AI behaviorconfiguration file matches logic of a preset AI behavior; testingwhether a result of running the AI behavior configuration file reaches apreset effect; and when the result of running the AI behaviorconfiguration file reaches the preset effect, implementing the preset AIbehavior according to the AI behavior configuration file.
 2. The methodaccording to claim 1, wherein before obtaining the AI behaviorconfiguration file, the method further comprises: encapsulating code forimplementing specific functions into components for implementing thespecific functions; storing, in node configuration, each component ofthe components for implementing the specific functions and one or moreof a type, a function, detailed information, an initializationparameter, and mounting topology, of the each component; and wherein theobtaining of the AI behavior configuration file comprises: calling theat least one preset component from the node configuration to configurethe AI behavior configuration file.
 3. The method according to claim 2,wherein the obtaining of the AI behavior configuration file comprises:calling the at least one preset component from the node configuration toconfigure a first AI behavior configuration file; obtaining a second AIbehavior configuration file from an AI rule library, wherein the AI rulelibrary has at least one AI behavior configuration file pre-storedtherein; and combining the first AI behavior configuration file and thesecond AI behavior configuration file to obtain the AI behaviorconfiguration file.
 4. The method according to claim 2, wherein theobtaining of the AI behavior configuration file comprises: obtaining theAI behavior configuration file from an AI rule library, wherein the AIrule library has at least one AI behavior configuration file pre-storedtherein.
 5. The method according to claim 2, wherein after calling theat least one preset component from the node configuration to configurethe AI behavior configuration file, the method further comprises:storing the obtained AI behavior configuration file in the AI rulelibrary.
 6. The method according to claim 1, wherein the testing ofwhether the result of running the AI behavior configuration file reachesthe preset effect comprises: loading application program environmentdata related to the preset effect; running the AI behavior configurationfile by integrating the application program environment data; previewingwhether the result of running the AI behavior configuration file isconsistent with the preset effect; and when the result of running the AIbehavior configuration file is consistent with the preset effect, theresult of running the AI behavior configuration file reaches the preseteffect.
 7. The method according to claim 1, further comprising: afterimplementing the preset AI behavior according to the AI behaviorconfiguration file, sending the AI behavior configuration file to an AIserver for the AI server to update an AI rule library, wherein: the AIrule library stores the AI behavior configuration file; the AI server ismounted with an AI system of at least one application program; the AIsystem of the at least one application program includes the AI rulelibrary; and the AI rule library stores at least one AI behaviorconfiguration file.
 8. An apparatus for implementing an AI behavior,comprising: an obtaining module configured to obtain an AI behaviorconfiguration file, wherein the AI behavior configuration file isconfigured using at least one preset component and the AI behaviorconfiguration file matches logic of a preset AI behavior; a testingmodule configured to test whether a result of running the AI behaviorconfiguration file reaches a preset effect; and an implementing moduleconfigured to, when the result of running the AI behavior configurationfile reaches the preset effect, implement the preset AI behavioraccording to the AI behavior configuration file.
 9. The apparatusaccording to claim 8, further comprising: an encapsulating moduleconfigured to encapsulate code for implementing specific functions intocomponents for implementing the specific functions; and a storing moduleconfigured to store, in node configuration, each component of thecomponents for implementing the specific functions and one or more of atype, a function, detailed information, an initialization parameter, andmounting topology, of the each component; and wherein the obtainingmodule is configured to call the at least one preset component from thenode configuration to configure the AI behavior configuration file. 10.The apparatus according to claim 9, wherein the obtaining module isconfigured to: call the at least one preset component from the nodeconfiguration to configure a first AI behavior configuration file;obtain a second AI behavior configuration file from an AI rule library,wherein the AI rule library has at least one AI behavior configurationfile pre-stored therein; and combine the first AI behavior configurationfile and the second AI behavior configuration file to obtain the AIbehavior configuration file.
 11. The apparatus according to claim 9,wherein the obtaining module is configured to: obtain the AI behaviorconfiguration file from an AI rule library, wherein the AI rule libraryhas at least one AI behavior configuration file pre-stored therein. 12.The apparatus according to claim 9, wherein the storing module isconfigured to store the obtained AI behavior configuration file in theAI rule library.
 13. The apparatus according to claim 8, wherein thetesting module comprises: a loading unit configured to load applicationprogram environment data related to the preset effect; a running unitconfigured to run the AI behavior configuration file by integrating theapplication pro gram environment data; a previewing unit configured topreview whether the result of running the AI behavior configuration fileis consistent with the preset effect; and a determining unit configuredto, when the result of running the AI behavior configuration file isconsistent with the preset effect, determine that the result of runningthe AI behavior configuration file reaches the preset effect.
 14. Theapparatus according to claim 8, further comprising: a sending moduleconfigured to send the AI behavior configuration file to an AI server,to cause the AI server to update an AI rule library, wherein: the AIrule library stores the AI behavior configuration file; the AI server ismounted with an AI system of at least one application pro gram; the AIsystem of the at least one application program includes the AI rulelibrary; and the AI rule library stores at least one AI behaviorconfiguration file.
 15. A non-transitory computer-readable medium havingcomputer program for, when being executed by a processor, performing amethod for implementing an artificial intelligence (AI) behavior, themethod comprising: obtaining an AI behavior configuration file, whereinthe AI behavior configuration file is configured using at least onepreset component and the AI behavior configuration file matches logic ofa preset AI behavior; testing whether a result of running the AIbehavior configuration file reaches a preset effect; and when the resultof running the AI behavior configuration file reaches the preset effect,implementing the preset AI behavior according to the AI behaviorconfiguration file.
 16. The non-transitory computer-readable mediumaccording to claim 15, wherein before obtaining the AI behaviorconfiguration file, the method further comprises: encapsulating code forimplementing specific functions into components for implementing thespecific functions; storing, in node configuration, each component ofthe components for implementing the specific functions and one or moreof a type, a function, detailed information, an initializationparameter, and mounting topology, of the each component; and wherein theobtaining of the AI behavior configuration file comprises: calling theat least one preset component from the node configuration to configurethe AI behavior configuration file.
 17. The non-transitorycomputer-readable medium according to claim 16, wherein the obtaining ofthe AI behavior configuration file comprises: calling the at least onepreset component from the node configuration to configure a first AIbehavior configuration file; obtaining a second AI behaviorconfiguration file from an AI rule library, wherein the AI rule libraryhas at least one AI behavior configuration file pre-stored therein; andcombining the first AI behavior configuration file and the second AIbehavior configuration file to obtain the AI behavior configurationfile.
 18. The non-transitory computer-readable medium according to claim16, wherein the obtaining of the AI behavior configuration filecomprises: obtaining the AI behavior configuration file from an AI rulelibrary, wherein the AI rule library has at least one AI behaviorconfiguration file pre-stored therein; and storing the obtained AIbehavior configuration file in the AI rule library.
 19. Thenon-transitory computer-readable medium according to claim 15, whereinthe testing of whether the result of running the AI behaviorconfiguration file reaches the preset effect comprises: loadingapplication program environment data related to the preset effect;running the AI behavior configuration file by integrating theapplication program environment data; previewing whether the result ofrunning the AI behavior configuration file is consistent with the preseteffect; and when the result of running the AI behavior configurationfile is consistent with the preset effect, the result of running the AIbehavior configuration file reaches the preset effect.
 20. Thenon-transitory computer-readable medium according to claim 15, furthercomprising: after implementing the preset AI behavior according to theAI behavior configuration file, sending the AI behavior configurationfile to an AI server for the AI server to update an AI rule library,wherein: the AI rule library stores the AI behavior configuration file;the AI server is mounted with an AI system of at least one applicationprogram; the AI system of the at least one application program includesthe AI rule library, and the AI rule library stores at least one AIbehavior configuration file.